With the explosion of digital music and digital video, consumers are faced with more and more options of media that they can purchase and/or access. Consumers are finding themselves overwhelmed with the masses of options of digital media from which they can pick.
As the cost of digital storage continues to drop, online vendors of media, particularly music, are finding that the incremental cost of increasing the number of digital media in their inventory is rapidly dropping. Thus online vendors are offering more and more content—expanding both the diversity of the content, but also the age and quantity of historical releases available. Combine this with the truly global nature of the internet, and the end result is a completely bewildering array of media that is immediately available for purchase and playing. New systems and methods are needed to enable consumers to search and explore this space, limiting the decision space to a size with which consumers are comfortable making decisions.
Current technologies and approaches for achieving the goal of limiting the space of options down to manageable sizes are either undesirable or incomplete. In one class of approaches, automatic content analysis (such as signal analysis for music) is used to extract features of the media, then correlate user tastes with those features; this approach is limited because the features on which user tastes are really based are often much more complex than can be inferred by a computer program.
In another class of approaches, a user's preferences are solicited on human defined metadata characteristics, and related media that matches those metadata characteristics that the user identifies with the most are selected. This approach fails because a) users' tastes are generally more complex then the rigid metadata allows, and b) one must first map all of the media onto values of the metadata characteristics, an expensive and time-consuming process.
Accordingly, there is a need for improved methods and systems for generating media recommendations for users that is cost-effective and scalable, and has the ability to capture complex tastes and adapt to new evolutions in taste.